Variable Selection for Model-Based Clustering
نویسندگان
چکیده
منابع مشابه
Variable Selection for Model-Based Clustering
We consider the problem of variable or feature selection for model-based clustering. We recast the problem of comparing two nested subsets of variables as a model comparison problem, and address it using approximate Bayes factors. We develop a greedy search algorithm for finding a local optimum in model space. The resulting method selects variables (or features), the number of clusters, and the...
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Variable selection for clustering is an important and challenging problem in high-dimensional data analysis. Existing variable selection methods for model-based clustering select informative variables in a "one-in-all-out" manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it cannot separate any of the clusters. In many applicat...
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ژورنال
عنوان ژورنال: Journal of the American Statistical Association
سال: 2006
ISSN: 0162-1459,1537-274X
DOI: 10.1198/016214506000000113